了解消費者的滿意度,對任何力求進步的商業組織而言可說是基本的要求。同樣地，在產品設計領域裡，這也是一個很重要的項目。隨著科技的進步，專業的分工，以往單純依靠經驗法則所做的決策已然不足以應付。同時也因為以消費者為中心和使用者導向的風潮漸漸地越來越受到重視。然而過往的研究經常會忽略掉消費者偏好之間的差異度，導致建構出來的模型往往不足以闡述現實生活中的狀況，亦難以實際地被應用。
本研究提出一個以感性工學為主的產品外型偏好預測模型，結合了消費者分群和資訊融合的概念。首先會以消費者之間異同的偏好應用Fuzzy C-means (模糊分類演算法)進行分群，求出每一消費者對於被劃分群組的隸屬程度，反映出實際情況裡的模糊性。接下來會使用Ordinal Regression(次序迴歸)來建立每一位消費者的偏好迴歸模型，基於一般大眾實際上比較不擅長如相關專家般為自身的喜好打上精準的分數，本研究讓使用者按照主觀意識將偏好的項目做排序並使用次序迴歸來建立個人預測的迴歸模型，並導入從模糊分群中求出各消費者對於群組的隸屬程度做為其影響權重，最終可利用於求出各群組對於研究對象的情感滿意度的狀況。此論文中以自行車做為演示預測模型的案例。

Understanding the customer of satisfaction, is a baseline standard of performance and a
possible standard of excellence for any business organization (Gerson, 1993) One of the most
important issues concerning product design is consumer satisfaction, the empirical decision is no
longer works sufficiently to assist industry/designer to have understanding therefore to have proper
strategy when doing design and decision making. The importance on understanding consumer
satisfaction in product design field is getting heavier as the concept of User-centered and
Consumer-oriented have been promoted. however the unlikeness of consumer preference always
out of consideration during previous researches ,thus the constructed prediction models aren’t fully
interpret the real situation and hardly applied on practical application.
This study proposes a prediction model based on Kansei Engineering, by applying the concept of
consumer segmentation and information consolidation. First, consumers will be separated into
different groups by applying FCM(fuzzy c-means) from their heterogeneous preference towards the studied object ,a membership showing the extent of each individual belonging towards group will be obtained to reflect the inherent vagueness of consumer segmentation. The extracted parameters
showing different extent of influence from consumers were then treated as retrieval when constructing a mutual satisfaction model for each cluster by applying Ordinal Regression. A case study of bicycle is demonstrated in this study to explore the effectiveness of the proposed model .

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